Camera Model Identification With The Use of Deep Convolutional Neural Networks

Amel Tuama 1 Frédéric Comby 1 Marc Chaumont 1
1 ICAR - Image & Interaction
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : In this paper, we propose a camera model identification method based on deep convolutional neural networks (CNNs). Unlike traditional methods, CNNs can automatically and simultaneously extract features and learn to classify during the learning process. A layer of preprocessing is added to the CNN model, and consists of a high pass filter which is applied to the input image. Before feeding the CNN, we examined the CNN model with two types of residuals. The convolution and classification are then processed inside the network. The CNN outputs an identification score for each camera model. Experimental comparison with a classical two steps machine learning approach shows that the proposed method can achieve significant detection performance. The well known object recognition CNN models, AlexNet and GoogleNet, are also examined.
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Submitted on : Thursday, October 27, 2016 - 6:25:36 PM
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Amel Tuama, Frédéric Comby, Marc Chaumont. Camera Model Identification With The Use of Deep Convolutional Neural Networks. WIFS: Workshop on Information Forensics and Security, Dec 2016, Abu Dhabi, United Arab Emirates. ⟨10.1109/WIFS.2016.7823908⟩. ⟨hal-01388975⟩

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